1. Technical Field
The present disclosure relates to speed limit assistant, and more particularly, to a speed limit assistant that uses both information from a camera and a global positioning system.
2. Discussion of Related Art
The technology of speed limit assistants (SLAs) has emerged as an important application for automotive advanced driver assistant systems (ADAS). The aim of such systems is to inform or warn the driver about effective speed limits, or in a more active setting, supplement an adaptive cruise control (ACC) system. Conventional solutions for SLAs use one of two different technologies: (i) camera based speed sign recognition or (ii) digitized maps with a global positioning system (GPS) interface.
Camera based speed sign recognition aims at detecting and classifying speed limit signs in image sequences. Camera sensors are becoming increasingly cheap and ubiquitous in automobiles. Since cameras are extremely versatile, camera based speed sign recognition is particularly attractive when combined with additional front facing camera ADAS applications, such as those involving the detection of lane markers, vehicles, or pedestrians.
One conventional camera based speed sign recognition system uses a two stage approach. In the first stage, image frames are scanned for possible sign occurrences using a detection algorithm (e.g., based on geometric or color information), and speed sign hypotheses are generated for regions with sufficient sign evidence. In the second stage, these hypotheses are verified and categorized using a statistical classifier. The classifier may be also able to eliminate false alarms from the first stage, based on the classifier's confidence value. However, the accuracy of this system is effected by a number of environmental factors. For example, factors such as adverse lighting (e.g., bright sunlight, complete darkness) and adverse weather (e.g., rain, snow) conditions can impede the accuracy of the system in correctly recognizing speed limit signs.
The second class of SLA technologies relies on a digitized map of speed limit zones, which, in combination with a GPS sensor, can provide the speed limit for the current vehicle position. The cost of such solutions is becoming more feasible, as an increasing number of vehicles are being equipped with navigation systems, and hence, a GPS sensor.
However, the accuracy of the GPS based system depends heavily on the accuracy of the provided map data. Since available maps are static, the GPS based system fails to provide the correct speed limits if speed limits change over time. For example, some modern traffic systems make use of variable message signs, which dynamically modify speed limits based on the current weather and/or traffic conditions. Further, construction zones require authorities to temporally lower the speed limits for the affected road section. Moreover, speed limit zones may not be completely mapped for every possible road. For example, coverage may be rather sparse for rural roads. Further, map data may only represent a snapshot at a certain time and may be outdated after a few years.
Thus, there is a need for methods and systems of assisting speed limit detection that combine information of both a camera and a GPS.